CAREER: Using Metamodeling to Enable High-Fidelity Modeling in Risk-based Multi-hazard Structural Design

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To assess the risk and resiliency of seismic and wind excited buildings in the United States, the use of high-fidelity computational models is paramount to characterizing the building performance. However, the need to propagate uncertainty through the system when estimating state-of-the-art risk/resiliency metrics significantly hinders, if not precludes, the use of such models. This difficulty becomes exasperated in risk-based decision-making where multiple building design solutions must be evaluated and compared over several hazards. The research goal of this Faculty Early Career Development Program (CAREER) award is to overcome this fundamental limitation through the investigation of a new simulation paradigm based on the optimal fusion of low-/intermediate-fidelity metamodels with high-fidelity structural models. By defining the metamodels through domain independent approaches, multi-hazard assessment will be naturally encompassed and will enable new approaches for rapidly identifying the optimal tradeoff solutions to multi-hazard risk-based decision problems. These advances will provide models and procedures for enabling a full transition to optimal risk-based design, while promoting the rational use of computational resources through rigorous optimization. Risk-based design will benefit national welfare and prosperity through enhancing the safety of the built environment against wind and seismic events to better protect life and property during extreme events and to maintain essential services and business continuities during response and recovery. The educational goals of this CAREER award are to increase the number of women in engineering and professionals with expertise in wind loss mitigation. This will be achieved through a high school outreach program that leverages the link between risk-based engineering and societal benefit to inspire a diverse student pool to pursue careers in engineering, the development of an undergraduate wind engineering program at the University of Michigan, and undergraduate student research opportunities. To implement the high school outreach program, project-based learning modules that connect risk-based engineering and societal benefit through basic science will be created. Dissemination of these materials will be achieved through a teacher training workshop. Data from this project will be archived in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://DesignSafe-ci.org). This research will create a new class of parametric metamodels (surrogate models) through identifying orthogonal subspaces for each high-fidelity computational model of the simulation environment. This will provide a setting in which both physics-based and data-driven reduced-order parametric metamodels can be defined through hyper-reduction and machine learning. The combined space of the high-fidelity and parametric metamodels will provide an enriched simulation environment in which multi-fidelity uncertainty propagation models can be defined for rapidly estimating high-fidelity probabilistic risk/resiliency metrics. The parametric nature of the metamodels will enable the creation of new adaptive multi-objective optimization schemes that will allow the rapid identification of high-fidelity multi-hazard Pareto fronts, which are central for effective risk-based decision-making. The models identified through this effort will directly benefit a number of other disciplines, including aerospace and biomedical engineering, atmospheric sciences, and the automotive industry, where rapid high-fidelity computation plays a key role in scientific discovery. The research will use the NHERI wind tunnel facility at the University of Florida.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.